Articles | Volume 12, issue 10
Research article
30 Sep 2021
Research article |  | 30 Sep 2021

Investigating spatial heterogeneity within fracture networks using hierarchical clustering and graph distance metrics

Rahul Prabhakaran, Giovanni Bertotti, Janos Urai, and David Smeulders


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on se-2021-45', Anonymous Referee #1, 11 May 2021
    • AC1: 'Reply on RC1', Rahul Prabhakaran, 28 Jul 2021
  • RC2: 'Comment on se-2021-45', David Sanderson, 21 May 2021
    • AC2: 'Reply on RC2', Rahul Prabhakaran, 28 Jul 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Rahul Prabhakaran on behalf of the Authors (19 Aug 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (20 Aug 2021) by David Healy
ED: Publish as is (21 Aug 2021) by Federico Rossetti(Executive Editor)

Post-review adjustments

AA: Author's adjustment | EA: Editor approval
AA by Rahul Prabhakaran on behalf of the Authors (23 Sep 2021)   Author's adjustment   Manuscript
EA: Adjustments approved (23 Sep 2021) by David Healy
Short summary
Rock fractures are organized as networks with spatially varying arrangements. Due to networks' influence on bulk rock behaviour, it is important to quantify network spatial variation. We utilize an approach where fracture networks are treated as spatial graphs. By combining graph similarity measures with clustering techniques, spatial clusters within large-scale fracture networks are identified and organized hierarchically. The method is validated on a dataset with nearly 300 000 fractures.